提交 c6ba87d9 编写于 作者: M mindspore-ci-bot 提交者: Gitee

!5804 fix bugs of op Fill, Equal, Eye, Conv2dTranspose and ExpandDims

Merge pull request !5804 from lihongkang/lhk_master
...@@ -139,7 +139,7 @@ class Validator: ...@@ -139,7 +139,7 @@ class Validator:
def check_int_range(arg_name, arg_value, lower_limit, upper_limit, rel, prim_name): def check_int_range(arg_name, arg_value, lower_limit, upper_limit, rel, prim_name):
"""Method for checking whether an int value is in some range.""" """Method for checking whether an int value is in some range."""
rel_fn = Rel.get_fns(rel) rel_fn = Rel.get_fns(rel)
type_mismatch = not isinstance(arg_value, int) type_mismatch = not isinstance(arg_value, int) or isinstance(arg_value, bool)
excp_cls = TypeError if type_mismatch else ValueError excp_cls = TypeError if type_mismatch else ValueError
if type_mismatch or not rel_fn(arg_value, lower_limit, upper_limit): if type_mismatch or not rel_fn(arg_value, lower_limit, upper_limit):
rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit) rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit)
......
...@@ -461,7 +461,8 @@ class Conv2dTranspose(_Conv): ...@@ -461,7 +461,8 @@ class Conv2dTranspose(_Conv):
width of the kernel. width of the kernel.
stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
the height and width of movement are both strides, or a tuple of two int numbers that the height and width of movement are both strides, or a tuple of two int numbers that
represent height and width of movement respectively. Default: 1. represent height and width of movement respectively. Its value should be equal to or greater than 1.
Default: 1.
pad_mode (str): Select the mode of the pad. The optional values are pad_mode (str): Select the mode of the pad. The optional values are
"pad", "same", "valid". Default: "same". "pad", "same", "valid". Default: "same".
......
...@@ -115,6 +115,8 @@ class ExpandDims(PrimitiveWithInfer): ...@@ -115,6 +115,8 @@ class ExpandDims(PrimitiveWithInfer):
>>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
>>> expand_dims = P.ExpandDims() >>> expand_dims = P.ExpandDims()
>>> output = expand_dims(input_tensor, 0) >>> output = expand_dims(input_tensor, 0)
[[[2.0, 2.0],
[2.0, 2.0]]]
""" """
@prim_attr_register @prim_attr_register
...@@ -887,6 +889,8 @@ class Fill(PrimitiveWithInfer): ...@@ -887,6 +889,8 @@ class Fill(PrimitiveWithInfer):
Examples: Examples:
>>> fill = P.Fill() >>> fill = P.Fill()
>>> fill(mindspore.float32, (2, 2), 1) >>> fill(mindspore.float32, (2, 2), 1)
[[1.0, 1.0],
[1.0, 1.0]]
""" """
@prim_attr_register @prim_attr_register
...@@ -2364,6 +2368,8 @@ class Eye(PrimitiveWithInfer): ...@@ -2364,6 +2368,8 @@ class Eye(PrimitiveWithInfer):
Examples: Examples:
>>> eye = P.Eye() >>> eye = P.Eye()
>>> out_tensor = eye(2, 2, mindspore.int32) >>> out_tensor = eye(2, 2, mindspore.int32)
[[1, 0],
[0, 1]]
""" """
@prim_attr_register @prim_attr_register
......
...@@ -2244,10 +2244,10 @@ class Equal(_LogicBinaryOp): ...@@ -2244,10 +2244,10 @@ class Equal(_LogicBinaryOp):
When the inputs are one tensor and one scalar, the scalar only could be a constant. When the inputs are one tensor and one scalar, the scalar only could be a constant.
Inputs: Inputs:
- **input_x** (Union[Tensor, Number, bool]) - The first input is a number or - **input_x** (Union[Tensor, Number]) - The first input is a number or
a bool or a tensor whose data type is number or bool. a tensor whose data type is number.
- **input_y** (Union[Tensor, Number, bool]) - The second input is a number or - **input_y** (Union[Tensor, Number]) - The second input is a number
a bool when the first input is a tensor or a tensor whose data type is number or bool. when the first input is a tensor or a tensor whose data type is number.
Outputs: Outputs:
Tensor, the shape is the same as the one after broadcasting,and the data type is bool. Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
......
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